Slides: Convolutional Neural Networks (20 min)

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• Convolutions

• Pooling

• U-Net

• Microscopy use cases

• Teaching methods

import numpy as np
import matplotlib.pyplot as plt

from ttt_workshop_cnn import utils

from IPython.display import HTML

Under the hood of CNNs

# %matplotlib widget
smiley = utils.draw_smiley()
kernel = utils.draw_gaussian_kernel(size=3, sigma=1)
animation = utils.animate_convolution(smiley, kernel, interval=200)
HTML(animation.to_jshtml())

Once Loop Reflect

Note how the output is smaller than the input. This can be avoided using padding

Padding

padded_smiley = np.pad(smiley, pad_width=1, mode='constant', constant_values=0)
padded_animation = utils.animate_convolution(padded_smiley, kernel, interval=200)
HTML(padded_animation.to_jshtml())

Once Loop Reflect

padded_smiley = np.pad(smiley, pad_width=1, mode='reflect')
padded_animation = utils.animate_convolution(padded_smiley, kernel, interval=200)
HTML(padded_animation.to_jshtml())

Once Loop Reflect

padded_smiley = np.pad(smiley, pad_width=1, mode='wrap')
padded_animation = utils.animate_convolution(padded_smiley, kernel, interval=200)
HTML(padded_animation.to_jshtml())

Once Loop Reflect